Exploiting the underlying structure of data has always been a key principle in data analysis. Its use has been fundamental to the success of machine learning solutions, from the translational equivariance of convolutional neural networks [1]} to the invariant attention mechanism in Alphafold [2]}. However, embedding invariances and symmetries in model architectures is hard in general and when possible, often incurs a high computational cost. This is the case, of rotation invariant neural network architectures that rely on group convolutions, which are feasible only for small, discrete transformation spaces or require coarse undersampling due to their high computational complexity [3]}, [4]}.
